Tài liệu Does relationship lending promote growth? Savings banks and SME financing pptx

Our findings indicate that a higher proportion of savings bank loans enhances firms togrow beyond rates which would be possible by internal or short-term financing only.. The use of trad

Does relationship lending promote growth?

Constantin F Slotty‡,†

Goethe University Frankfurt, House of Finance, Germany

First Draft: January 2009 This Version: April 2009

AbstractThis paper addresses the question whether close borrower-lender relationships,

so called hausbank-relationships, facilitate the funding and beneficial development

of SME To this end, we derive a model which relates a firm’s growth rate to its needfor external funds and subsequently compute the firms that exceed their predictedgrowth rate We then use this measure to identify specific characteristics that areassociated with long- and short-term financing of firm growth, in particular theinfluence of relationship lending We find that close ties with savings banks predictfirms’ access to external finance to fund growth Moreover, the long-term liabilities

of firms with hausbank-relationships almost double those with multiple relationshipswhile the overall leverage is about the same In turn, we find an strong empiricalrelationship between the provision of long-term funds and firm growth

Keywords: Small business lending, credit access, public banks

JEL Codes: G21, D21

∗ This research paper is part of a project funded by the German Savings Bank Association The expressed opinions are strictly those of the author and do not necessarily reflect those of the affiliated organizations.

‡ Goethe University Frankfurt, House of Finance, Email: slotty@finance.uni-frankfurt.de

† I thank Michael Koetter for helpful discussion.

Electronic copy available at: http://ssrn.com/abstract=1376251

We aim to provide empirical evidence on the apparent conundrum regarding public bank’scontribution to the performance of small and medium enterprises (SME) Specifically, wetest one of the main reasons put forward by savings banks in respect to their beneficialimpact on the business landscape in a developed economy: do German savings banksfacilitate the funding and beneficial development of SME?

The role of banks to provide corporate firms with access to financial funds remainscrucial in most developed economies (Hackethal, Schmidt, and Tyrell 1999) SpecificallySME, which frequently form the backbone of the economy, rely on banks to fuel theirgrowth (Berger, Klapper, and Udell 2001; Samitas and Kenourgios 2004) According toAudretsch and Elston (2002), both the role of SMEs and banks is particularly importantfor the third largest economy of the world: Germany

At the same time, the German banking system exhibits some distinct characteristicscompared to other industrialized countries Specifically, the share of total assets managed

by publicly owned savings banks is relatively large (Koetter et al 2006) The relativemerits and concerns regarding public banks, however, continue to fuel a heated, andsometimes even ideological, debate among both practitioners and academics But thescientific evidence provides mixed guidance to this debate On the one hand, a number ofstudies report that public banks are less profitable and more risky than privately ownedbanks (Iannotta, Nocera, and Sironi 2007) On the other hand, other empirical stud-ies that distinguish, for example, developed and developing countries find no significantrelation between public ownership and profitability (Micco, Panizza, and Yanez 2007)

In response to the ongoing policy debate as well as the mixed economic evidence, publicbanks in general, and German savings banks in particular, highlight their contribution tothe economy as follows: to establish and maintain steady relations especially with SME,which might otherwise be shut-off external sources of finance

Theoretical evidence if intense bank-firm relationships are beneficial to the latter mains unclear a priori Boot and Thakor (2000) illustrate the ambivalence of relationshipbanking The lock-in effect can be to the firm’s detriment: proprietary knowledge of bor-rower characteristics by the bank paired with less alternatives to evade re-negotiability

re-of sre-oft budget constraints re-of firms with few banking relations can jeopardize both banks’and firms’ incentives In turn, long-term relations can enhance the efficiency of credit

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contracts and may provide access to external funds during crises, too.

The empirical evidence on the relation between firm performance and bank-firm lations mirrors the theoretical ambiguity For example, Berger et al (2007) report forIndian state-owned banks that these do not serve opaque small borrowers significantlymore often compared to other customer groups In turn, they find evidence that corpo-rates maintaining relations with state-owned banks have few bank relations and rely onthese to a larger extent In turn, D’Auria, Foglia, and Reedtz (2007) report for Italianbanks that hausbank-relations enable firms to borrow at lower cost Likewise, Cole (1998)finds for the U.S that SME with existing relationships to banks are more likely to receivefurther credit, thus underpinning the value of private information generated by an arm’slength potential lender The ambiguity of the international empirical evidence is reflected

by findings of Agarwal and Elston (2001) on German firm performance While they port that German firms enjoy easier access to capital, their results do neither show higherprofitability nor growth for these firms

re-In light of the mixed empirical evidence, we attempt to provide insights based onconfidential data obtained from the German Savings Bank Association We seek to assessmore directly the hypotheses that savings banks support especially more constrained SMEand the question to what extent close borrower-lender relationships are beneficial to thedevelopment of these firms The involvement of savings banks in this regard can consist ofseveral layers; the channeling of government aids, continued operative business mentoring,provision of liquidity insurance in situations of unexpected borrower rating deteriorationand long-term credit contracts As suggested by Elsas (2005) we use the dependency onsavings bank debt as proxy for hausbank-relationship and predict firms’ excess growthbased either only on internal or short-term funding

Our findings indicate that a higher proportion of savings bank loans enhances firms togrow beyond rates which would be possible by internal or short-term financing only Theseresults hold up to different model specifications and hausbank-relationship proxies Sinceour sample consists entirely of savings banks clients the results apply only to hausbank-relationships of firms with savings banks

The outline of the paper is as follows Section 2 introduces the data and summarystatistics Section 3 provides an overview over the measures of the constraint growthrates and examines the implications that arise for the SME in our sample In section 4

we present the methodology and discuss the variables used in the regressions Our resultsare reported in section 5 and section 6 concludes.

The firm-level data covers financial statements of SME from all federal states in Germany.Most of the firms in our sample are rather small (with average total assets of e1,091,409)thus reflecting a representative picture of the German SME landscape The unbalancedsample consists of 467,033 firm observations averaged over the period from 1996 – 2006and has been provided by the German Savings Bank Association (DSGV) All firms in thesample are savings banks clients with differing degrees of savings bank loans However,the data does not contain information about the number or type of the other lenders Forthe gross domestic product (GDP) of the respective regions the data is complemented bythe Federal and State statistical offices data (DeStatis) To control for the competitivebehavior of savings banks in Germany we calculate Lerner indices from the financialstatements of savings banks

Figure 1: Proportion of micro, small and medium–sized firms by years

Figure 1 shows the proportion of micro, small and medium-sized firms in the sample According to the definition of the European Commission a micro (small/ medium–sized) firm is constituted by a headcount with a maximum of 10 (50/ 250) full–time equivalents (FTE), a turnover below e2m (10/ 50) or a balance sheet total less than e2m (10/ 43).

In Table 1 we present the median and mean values of a number of relevant features

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Table 1: Descriptives by degree of dependency on savings bank credit

Table 1 reports the medium and mean values (in parentheses) The figures are reported in quartiles by the degree of financial dependency on savings banks, i.e the proportion of savings bank loans to total bank liabilities The leverage is calculated

by total debt divided by total assets, long term credit are all debt maturities over 5 years over total assets, average cost of interest by interest expenses over total debt, interest coverage by earnings before interest and taxes (EBIT) over interest and lease expenses and trade credit by accounts payable over total debt The table comprises 467,033 firm observations.

Median (mean) values Savings banks loans to total bank loans

(8,105,090) (4,137,974) (2,773,842) (1,594,725) (4,152,908)

of the SME in our sample The values are averaged over the observation period andare reported by the degree of the credit-relationship with savings banks First of all, wesee that the SME in our sample are quite highly leveraged with a ratio of debt to totalassets of 82% and average interest cost of 4.8% Although firms with a high share ofsavings banks loans pay marginally higher interest rates they seem to have less problemsaccommodating their financial obligations (including leases) as depicted by the higherinterest coverage ratios The use of trade credit with a median of 11% is rather low

in comparison to SME in other economies such as Spain where short-term non-bankfinancing makes up about 65% of total firm debt (González, Lopez, and Saurina 2007).The higher share of savings bank debt financing for small firms suggests that these firmsare more likely to have hausbank-relationships with their respective savings bank (Elsas2005) This suggestive evidence is further corroborated by the higher long-term creditratios of companies with a share of savings banks financing above 75% which unperpinthe long-term implicit contracts between a hausbank and its debtors Table 2 provides adescription of the nexus of capital intensity, return on assets before tax (RoA) and savingsbanks financing and puts these figures into perspective

Table 2: RoA (median) over states, savings banks dependency and capital intensityTable 2 depicts the return on assets before tax (RoA) over the period 1996 – 2007 by federal states split into the capital-intensity (CI) of the respective firms and their share of savings bank loans of all bank loans The CI, in turn, is calculated as the ratio of property, plant and equipment (PPE) to total assets by quartiles (e.g CI 1 depicts firms with a ratio of PPE to total assets up to 25%) On the right hand side the observations per state as well as the average RoA per state are reported.

1996 – 2006 25%< savings banks loans 25%<50% savings banks loans 50%<75% savings banks loans 75%<100% savings banks loans

State CI 1 CI 2 CI 3 CI 4 CI 1 CI 2 CI 3 CI 4 CI 1 CI 2 CI 3 CI 4 CI 1 CI 2 CI 3 CI 4 Obs RoA Schleswig-Holstein 3.8% 3.2% 1.9% 0.7% 2.9% 4.1% 4.4% 1.8% 3.0% 4.8% 4.4% 2.4% 5.5% 5.6% 4.8% 3.4% 15,256 3.6% Lower Saxony 2.8% 3.8% 3.6% 1.2% 3.5% 4.3% 4.0% 2.1% 3.4% 4.4% 4.8% 2.3% 5.2% 5.5% 4.5% 2.8% 49,125 3.6% North Rhine-

Westphalia

3.1% 4.3% 3.6% 1.6% 4.0% 4.7% 3.7% 2.0% 4.2% 5.6% 4.7% 3.0% 5.9% 6.6% 6.1% 3.8% 63,087 4.2% Hesse 2.7% 3.5% 3.1% 1.5% 3.1% 3.8% 3.2% 1.4% 3.5% 4.8% 3.9% 2.6% 4.9% 4.9% 4.8% 4.0% 42,423 3.5% Rhineland-Palatinate 2.6% 3.8% 2.4% 0.4% 3.1% 3.5% 2.8% 1.6% 3.5% 4.1% 4.7% 2.1% 5.5% 5.9% 5.4% 3.3% 32,363 3.4% Saarland 2.1% 3.2% 2.1% 1.9% 3.1% 4.8% 2.4% 1.5% 2.9% 4.4% 2.4% 2.6% 4.2% 4.3% 4.0% 3.2% 11,457 3.1% Baden-Württemberg 3.5% 4.1% 3.9% 2.4% 3.7% 5.1% 4.7% 2.6% 4.1% 5.7% 5.2% 3.5% 6.5% 7.0% 6.0% 4.7% 109,157 4.5% Bavaria 2.8% 3.5% 3.3% 1.7% 3.2% 4.4% 4.6% 2.0% 3.6% 4.5% 4.8% 2.7% 5.6% 6.2% 6.1% 4.1% 109,084 3.9% Obs West 21,932 15,908 10,980 5,808 22,671 15,044 8,543 4,517 26,973 17,187 10,133 5,026 135,600 73,974 54,905 37,023 431,952 - Average West 2.9% 3.7% 3.0% 1.4% 3.3% 4.3% 3.7% 1.9% 3.5% 4.8% 4.4% 2.6% 5.4% 5.8% 5.2% 3.7% - 3.7% Mecklenburg-

Western Pomerania

1.4% 3.8% 2.1% 1.4% 3.6% 4.2% 6.5% 0.7% 2.1% 3.8% 4.6% 0.0% 3.8% 4.0% 4.2% 2.3% 1,703 3.0% Brandenburg 2.8% 2.1% 1.9% -0.2% 2.6% 3.4% 2.8% 0.2% 2.2% 3.0% 3.3% 1.1% 3.2% 3.8% 2.5% 1.7% 11,225 2.3% Saxony-Anhalt 1.9% 2.1% 1.9% 0.5% 3.2% 2.7% 3.2% 0.5% 2.5% 3.4% 2.4% 1.1% 2.7% 3.1% 2.7% 1.4% 12,861 2.2% Thuringia 2.6% 1.9% 2.7% 0.2% 3.2% 4.2% 3.3% 0.8% 3.1% 3.5% 3.0% 3.1% 3.3% 3.2% 3.2% 1.9% 7,677 2.7% Saxony 1.8% 3.3% 2.1% 0.3% 3.2% 3.4% 3.1% 1.3% 4.3% 3.4% 4.8% 2.1% 4.5% 4.1% 3.6% 2.6% 15,792 3.0% Obs East 1,574 1,984 1,740 1,212 1,663 1,914 1,260 686 2,084 2,397 1,721 725 8,688 8,824 7,739 5,047 49,258 - Average East 2.1% 2.6% 2.1% 0.4% 3.2% 3.6% 3.8% 0.7% 2.8% 3.4% 3.6% 1.5% 3.5% 3.7% 3.3% 2.0% - 2.6% Obs All 23,506 17,892 12,720 7,020 24,334 16,958 9,803 5,203 29,057 19,584 11,854 5,751 144,288 82,798 62,644 42,070 481,210 - Average All 2.5% 3.2% 2.6% 0.9% 3.2% 4.0% 3.8% 1.3% 3.2% 4.1% 4.0% 2.1% 4.4% 4.7% 4.2% 2.8% - 3.2%

An inspection yields several interesting findings: First, we see that firms with a capitalintensity in the second quartile (a proportion of fixed assets to total assets of 25%<50%)are in almost every state and every proportion of savings banks loans the most profitablecompanies in the sample To find an explanation for this finding it would be interesting

to consider the industries that lie within this capital intensity range to draw conclusions.However, due to the anonymized nature of the sample this information was not available.Secondly, the average profitability within each capital intensity quartile rises with theproportion of savings banks loans Since we know, that these firms have a closer borrower-lender-relationship with at least one bank, a possible explanation could be that betteraccess to external financing enables them to seize profitable investment opportunitieswhich, in turn, leads to higher RoA’s Lastly, we observe that firms in the western regions

of Germany have a higher average profitability of 0.9% which could be driven by a slowergrowth of the economy in the eastern states (Ludwig 2006).1

Our aim is to examine the impact of close borrower-lender relationships with savingsbanks on financial constraints and ultimately firm growth However, firms are not equallyaffected by the presence of financial constraints First, companies with sufficient cash flowsfrom operations to fund profitable investments are less affected than firms whose internalresources do not suffice to accommodate their financial requirements Second, in the vein

of Rajan and Zingales (1998) firms from some industries have higher equilibrium leverageratios Ideally, we would therefore differentiate, say, capital intensive manufacturing firmsfrom service oriented business Due to missing data on industry codes, we thereforeestimate a predicted growth rate for each firm, relying either only on its internal funds or

on short-term financing Then, to assess whether better access to external funding enablesfirms to seize growth opportunities, we first need to identify firms that require externalfinancing and investigate whether their realized growth is contingent on the provision of

1 To test whether the median of the RoA’s in the respective groups are in fact different of each other we conduct a two-sample Wilcoxon rank-sum (Mann-Whitney) test The H 0 -Hypothesis is that the median

of the RoA in the fourth quartile (75%<100% savings banks loans) is the same as the one in the remaining groups (0%<75% savings banks loans) The test results give strong evidence to reject the null hypothesis (significant at the 1% level) suggesting that the higher median RoA’s for firms with a proportion of savings bank loans above 75% are not caused by random fluctuation.

(long-term) financing by savings banks.2

Demirgüç-Kunt and Maksimovic (1998) point out that both the firm’s cash flow andits optimal investment level are endogeneous They illustrate this proposition by theexample of a capital intensive firm which is in need of larger investment expenditures tofund further growth If the firm’s products face high demand or the market power ofthat company is sufficiently high, it may be able to finance its growth only from internalresources Another firm, on the other hand, with the same properties but facing lessfavorable prospects may need external financing in order to attain the same growth rate

To account for this endogeneity, we use two types of predicted firm growth First, ameasure that predicts the maximum growth rate if a firm only relies on its internal fundsand second a measure for firms that can also resort to short-term financing Subsequently,

we test the hypothesis that firms which experience sufficient demand can exceed their dicted growth rates by obtaining (long-term) savings banks financing In the development

pre-of the model we follow suggestions pre-of cross-country firm-level studies by Demirgüç-Kuntand Maksimovic (1998, 2002) First, we derive a growth measure based on Higgins (1977)which describes the maximum growth if a firm retains all earnings and finances investmentonly from internal sources of finance (constraints on short- and long-term financing) Thisinternal growth rate IGR equals:

where RoA denotes return on assets In turn, if firms use also short-term funding to fundgrowth, the second firm growth benchmark equals the firms return on long-term assets

LT A, where the latter equals total assets less short-term debt:

Based on equations (1) and (2), we then follow Demirgüç-Kunt and Maksimovic (2002)and create for each firm i in region r at time t an indicator variable, whether realizedgrowth exceeded predicted growth

2 As a further robustness check we also followed Rajan and Zingales (1998) who calculated benchmark growth rates based on industry codes We attempted to substitute these by benchmark growth rates based

on quartiles of capital intensity and regional differences However, the results came out inconclusive which suggests that this measure is too crude to predict the appropriate growth rate for industries within a given capital intensity.

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However, the eventual existence of spare capacity in firm’s production process poses

a potential problem to our model We attempt to mitigate this problem by averagingthe afore generated indicator variables over all observations for each firm in order tosmooth out production Thus for each firm we obtain one measure for the excess growthwith internal and one for short-term funding This variable is in turn used as dependentvariable in a regression model, which is explained by the proportion of savings bankscredit of the respective firm and further control variables

Further, our model makes several assumptions which may underestimate the maximumattainable growth rate and overestimate its cost; it assumes that the firms’ use of theirunconstrained sources of finance in relation to total assets does not change over theobservation period and that the production technology desists from advancements thatmight reduce the cost of replacement investments

Table 3 presents for each firm size category and by federal states the proportion of firmswhich exceed their internal and short-term growth rates We derive these figures by firstcalculating a dummy variable for each firm and year, that equals one if the annual growthrate of sales exceeds the maximum attainable internal (IGRit) or short-term borrowing(SGRit) growth rate respectively Thus, we obtain the dummy variable (ST GROit) if

a firm exceeds its internal growth rate and (LT GROit) if a firm exceeds its short-termfinanced growth rate in a given year Subsequently, the dummy variables are averagedover the observation period to obtain a metrical scaled variable for each firm ranging from

0 to 1

By using the same firm size classification as the European Commission, Table 3 amines whether firms of different size also exhibit different growth properties We seethat approximately 40% of all firms in our sample exceed their internal growth rates.Larger firms tend to exceed their growth rates (IGR and SGR) more often than smallerfirms, potentially due to easier access to finance to facilitate growth Moreover, a higherproportion of firms in the eastern regions of Germany exceed their growth rates in com-parison to the western states (48.5% vs 42.7% for IGR and 44.8% vs 36.3% for SGR).This may be due to lower levels from which eastern firms start to grow accordingly faster

ex-As Demirgüç-Kunt and Maksimovic (1998) noted, access to long-term financing seems to

be particularly important for (large) German firms Our sample of smaller firms exhibitssimiliar properties; if we take, for instance, the 33.2% of micro SME in the western regions

Table 3: Proportion of firms growing faster than predictedTable 3 presents the proportion of firms by states whose mean annual growth rate of sales exceeds the means of their constrained growth rates (IGR and SGR) For each firm the internal growth rate (IGR t is given by (RoA t /(1 − RoA t )) where RoA t is the firm’s return on assets before tax Maximum short-term financed growth rate (SGR t ) is defined as RoLT A t /(1 − RoLT A t ) where RoLT A t is the ratio of earnings before tax to long-term capital The firms are divided into three different size ranges in accordance with the definition of the European Commission A micro (small/ medium–sized) SME is constituted by a headcount with a maximum of 10 (50/ 250) full–time equivalents (FTE), a turnover below e2m (10/ 50) or a balance sheet total less than e2m (10/ 43).

Proportion of firms that exceed their:

Internal growth rate Short-term financed growth rate

In addition to firm size effects on growth, it is ultimately the impact of relationships we are interested in In Table 4 we examine the constraint growth ratesSGR and IGR by the proportion of savings bank loans to total loans and by federalstates We see that the pattern of rising predicted growth rates of eastern and westernGerman states by the proportion of savings banks loans is similar to the observed valuesfor the RoA’s in Table 2 Moreover, the majority of firms (52.7%) in our sample seem

hausbank-to have close ties with their savings bank as depicted by the high number of companies

in the 10th decile Strikingly, the growth rates SGR as well as IGR increase almostmonotonically for each state; the mean values of SGR and IGR roughly double from the1st to the 10th decile This finding leads to the question whether the higher predicted

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Table 4: Internal and short-term financed growth ratesTable 4 presents the short-term (SGR) and internal (IGR) financed growth rates of firms by deciles of savings bank loans

to total bank loans The first row in each federal state presents the SGR and the second row the IGR The further we go right the higher the proportion of savings banks loans to total bank loans Column "10", for instance, shows the SGR and IGR of firms with over 90% savings banks loans for each state respectively.

1996–2006 Proportion of savings bank loans to total bank loans in deciles

3.2% 3.5% 3.1% 4.1% 3.9% 4.0% 4.0% 4.6% 4.3% 5.3% 4.0% North Rhine-

Westphalia

6.7% 7.3% 7.8% 8.7% 9.0% 9.1% 10.8% 10.7% 11.2% 11.7% 62,652 9.3% 3.2% 3.7% 3.9% 4.2% 4.3% 4.5% 5.2% 5.1% 5.6% 6.6% 4.6% Hesse 6.4% 6.2% 7.2% 6.6% 6.8% 6.8% 8.5% 8.8% 8.9% 8.7% 42,068 7.5%

2.9% 3.1% 3.3% 3.3% 3.4% 3.5% 4.1% 4.6% 4.7% 5.2% 3.8% Rhineland-Palatinate 5.5% 6.6% 6.5% 6.5% 7.1% 6.9% 9.2% 8.7% 9.6% 9.9% 32,046 7.7%

2.7% 3.2% 2.7% 3.1% 3.2% 3.3% 4.4% 4.5% 5.0% 5.9% 3.8% Saarland 5.1% 5.2% 7.9% 7.1% 6.5% 5.6% 7.7% 8.2% 8.0% 8.3% 11,439 7.0%

2.2% 2.6% 3.4% 3.3% 3.0% 2.3% 3.3% 3.7% 4.0% 4.6% 3.2% Baden-Württemberg 7.4% 8.2% 8.5% 9.5% 9.1% 9.5% 10.0% 10.8% 10.7% 12.5% 108,604 9.6%

3.7% 4.0% 4.3% 4.5% 4.4% 4.8% 5.0% 5.6% 5.7% 7.2% 4.9% Bavaria 6.2% 6.9% 6.5% 7.4% 8.5% 8.2% 7.9% 8.6% 8.9% 10.9% 108,152 8.0%

3.1% 3.3% 3.2% 3.7% 4.2% 4.1% 4.2% 4.5% 4.9% 6.5% 4.2% Obs West 21,426 19,246 18,490 18,451 19,047 20,033 22,555 26,029 35,112 228,277 428,666 Mean SGR 6.1% 6.7% 7.2% 7.8% 7.8% 7.7% 8.6% 9.1% 9.2% 10.0% 8.0% Mean IGR 3.0% 3.3% 3.4% 3.8% 3.7% 3.8% 4.2% 4.6% 4.9% 5.8% 4.1% Mecklenburg-

Western Pomerania

3.6% 3.3% 4.4% 8.0% 9.8% 6.0% 8.2% 5.4% 9.1% 5.3% 1,678 6.3% 2.3% 2.0% 2.7% 4.8% 5.2% 2.9% 4.3% 3.3% 5.6% 3.4% 3.7% Brandenburg 1.5% 2.5% 5.3% 6.9% 4.7% 6.1% 5.5% 4.8% 5.6% 5.0% 11,097 4.8%

1.0% 1.5% 2.6% 2.8% 2.3% 3.1% 3.0% 2.8% 3.1% 3.1% 2.5% Saxony-Anhalt 3.1% 4.4% 4.0% 7.4% 5.5% 5.2% 4.9% 5.1% 4.7% 4.4% 12,584 4.9%

1.4% 1.9% 1.8% 3.3% 3.1% 2.6% 2.8% 2.8% 2.7% 2.8% 2.5% Thuringia 3.2% 3.7% 5.9% 5.2% 5.7% 4.8% 6.2% 6.0% 7.4% 4.6% 7,610 5.3%

1.7% 2.1% 3.0% 2.8% 3.3% 2.7% 3.4% 3.4% 4.3% 3.0% 3.0% Saxony 2.9% 5.8% 5.8% 6.5% 5.9% 6.4% 7.9% 7.7% 7.4% 5.8% 15,582 6.2%

1.5% 3.1% 2.7% 3.3% 3.1% 3.7% 4.4% 4.5% 4.3% 3.8% 3.4% Obs East 2,907 2,399 2,102 2,142 2,311 2,435 2,861 3,355 4,672 23,367 48,551

Mean SGR 2.9% 3.9% 5.1% 6.8% 6.3% 5.7% 6.5% 5.8% 6.8% 5.0% 5.5% Mean IGR 1.6% 2.1% 2.6% 3.4% 3.4% 3.0% 3.6% 3.4% 4.0% 3.2% 3.0% Obs All 24,333 21,645 20,592 20,593 21,358 22,468 25,416 29,384 39,784 251,644 477,217

growth rates also lead to higher excess growth for (augmented) savings bank financedSME Table 5 attempts to give a first, descriptive insight Likewise Table 3, we see thatSME in the eastern states more often exceed their internal and short-term financed growthrates Further, the spread of firms’ internally and short-term financed excess growth ratesyields some interesting findings: Throughout all deciles the spread between ST GRO and

LT GRO is higher in the western states This suggests that on average firms in the easternstates have a greater exigency to fund their growth with long-term loans Moreover, thespreads are declining for all states from the 1st to the 10th decile indicating that firmswith a higher proportion of savings banks loans use long-term funding more often to

finance their growth Yet, the most apparent observation are the declining excess growthrates from the 1st to the 10th decile.

Table 5: Excess growth rates by share of savings bank loansTable 5 presents the median of the excess growth variables ST GRO i and LT GRO i by deciles of savings bank loans and by federal states The first row in each federal state presents the proportion of firms that grow at average rates exceeding the IGR rate while the second row shows the analogous data for the proportion of firms above their SGR rate.

1996–2006 Proportion of savings bank loans to total bank loans in deciles

37.7% 34.9% 36.5% 33.4% 33.2% 34.7% 33.7% 31.5% 31.4% 30.7% 33.8% North Rhine-

Westphalia

44.3% 41.1% 40.4% 39.6% 38.3% 37.5% 35.4% 35.5% 34.2% 32.5% 62,652 37.9% 37.3% 34.3% 33.8% 32.9% 31.8% 31.4% 29.2% 29.9% 28.4% 28.1% 31.7% Hesse 42.2% 42.8% 42.5% 41.2% 41.3% 39.3% 37.1% 35.7% 33.5% 32.3% 42,068 38.8%

36.1% 37.2% 35.9% 35.2% 36.1% 33.8% 31.9% 30.7% 29.0% 29.2% 33.5% Rhineland-Palatinate 46.5% 42.4% 41.8% 40.2% 39.4% 39.6% 36.9% 34.4% 34.5% 32.5% 32,046 38.8%

39.4% 37.3% 36.6% 35.4% 33.7% 34.2% 31.6% 29.8% 30.1% 28.9% 33.7% Saarland 45.0% 47.1% 42.1% 43.9% 44.8% 45.7% 43.3% 42.9% 38.9% 36.3% 11,439 43.0%

39.4% 42.2% 37.4% 40.0% 39.7% 41.1% 38.7% 38.6% 35.3% 33.0% 38.5% Baden-Württemberg 40.8% 40.1% 39.1% 38.1% 38.9% 38.4% 36.0% 35.1% 34.4% 32.6% 108,604 37.3%

34.0% 34.2% 33.1% 32.1% 32.1% 32.1% 30.3% 29.3% 29.1% 28.2% 31.4% Bavaria 42.5% 40.1% 39.3% 38.7% 37.3% 37.5% 36.9% 35.4% 34.1% 31.9% 108,152 37.4%

36.9% 34.6% 34.3% 34.0% 32.4% 32.3% 32.0% 30.8% 30.1% 28.4% 32.6% Obs West 21,426 19,246 18,490 18,451 19,047 20,033 22,555 26,029 35,112 228,277 428,666 Mean STGRO 37.6% 36.4% 35.1% 34.6% 34.3% 34.1% 32.4% 31.5% 30.6% 29.4% 33.6% Mean LTGRO 44.1% 42.3% 40.9% 40.0% 39.8% 39.3% 37.3% 36.3% 35.1% 33.0% 38.8% Mecklenburg-

Western Pomerania

53.5% 48.9% 48.6% 43.7% 37.5% 44.8% 34.9% 42.3% 34.2% 34.2% 1,678 42.3% 52.1% 47.4% 42.6% 38.6% 32.7% 42.7% 33.6% 38.7% 32.0% 31.9% 39.2% Brandenburg 49.9% 47.8% 43.6% 39.8% 40.7% 39.9% 39.0% 38.8% 38.3% 35.1% 11,097 41.3%

45.0% 43.4% 39.0% 35.7% 36.1% 37.6% 35.5% 36.8% 35.8% 33.5% 37.8% Saxony-Anhalt 47.9% 49.5% 44.5% 44.4% 42.0% 40.5% 39.6% 38.4% 38.4% 36.8% 12,584 42.2%

43.8% 46.2% 39.1% 38.5% 37.9% 37.7% 36.6% 35.9% 36.1% 35.3% 38.7% Thuringia 49.3% 47.2% 42.3% 46.6% 45.1% 41.0% 37.3% 41.1% 37.4% 36.7% 7,610 42.4%

45.8% 42.3% 37.9% 42.8% 39.8% 36.3% 32.8% 36.2% 33.0% 34.4% 38.1% Saxony 49.8% 44.6% 46.4% 43.8% 41.6% 38.6% 37.2% 38.2% 33.8% 35.2% 15,582 40.9%

45.3% 40.3% 41.8% 38.9% 36.7% 35.0% 33.1% 34.3% 30.0% 32.7% 36.8% Obs East 2,907 2,399 2,102 2,142 2,311 2,435 2,861 3,355 4,672 23,367 48,551

Mean STGRO 46.4% 43.9% 40.1% 38.9% 36.6% 37.9% 34.3% 36.4% 33.4% 33.6% 38.1% Mean LTGRO 50.1% 47.6% 45.1% 43.7% 41.4% 41.0% 37.6% 39.7% 36.4% 35.6% 41.8%

by a regression analysis that accounts for multiple factors and will be adressed in section5

12

we first estimate below the logit model for each state separately and subsequently forthe whole sample Note that within each state we observe mostly multiple savings bankregions j For these we therefore also include region-specific controls As such our result

is analogous to the cross-country perspective in Demirgüç-Kunt and Maksimovic (1998).For reasons of simplification, the right hand side of the equations presented within thefollowing tables generally depicts the exponential term in our logit regression

Firm characteristics X We specify the following firm-specific variables Our primaryvariable is the proportion of a firm’s savings bank loans to all bank loans (SB) Whited(1992) found that financial constraints and thus, a diminished ability to access exter-nal financing, has a direct influence on firms’ investment plans Therefore our variabledescribes the dependency of a firm on its savings bank and aims to test whether haus-bank-relationships help firms to seize their growth options

The rationales for the benefits of close borrower-lender relationships are suggested inthe financial intermediation literature: increased credit availability, intertemporal smooth-ing, enhancement of borrower’s project payoffs and liquidity insurance as well as moreefficient decisions in case of financial distress (e.g Sharpe (1990), Petersen and Rajan(1995), Boot and Thakor (2000), Elsas (2005)) Since we consider two measures of con-traint growth (ST GRO and LT GRO) it would be conceivable that hausbank-relationshipshave a mixed impact A positive relation, for instance, with firm growth relying only oninternal funds but no significant relation with firm growth if firms also have access toshort-term borrowing would indicate that savings banks on average only provide short-term funding to their customers Conversely, a significant relation for the savings bank